Machine Learning-Based Approaches for Breast Cancer Detection in Microwave Imaging

Humza Sami, Mahnoor Sagheer, Kashif Riaz, Muhammad Qasim Mehmood, Muhammad Zubair

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Detection of breast cancer at an early stage can significantly reduce the mortality rate. Microwave imaging is a promising detection tool for harmless and non-ionizing screening of breast cancer. In this work, a fast and accurate machine learning algorithm is proposed for the prediction of the breast lesion using microwave signals. Machine learning has proved itself reliable in the field of biomedical application where the diagnosis of the disease is desired. The support vector machine (SVM) algorithm with the linear and polynomial kernel is trained and tested on raw backscattered signals data. SVM with third-degree polynomial kernel obtained 99.7% accuracy that outperforms the existing conventional machine learning binary classification algorithms. Thus, the prediction of tumor presence would help the radiologist to diagnose tumor correctly at early stages.
Original languageEnglish (US)
Title of host publication2021 IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), USNC-URSI 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages72-73
Number of pages2
ISBN (Print)9781946815101
DOIs
StatePublished - Jan 1 2021
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-20

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